Efficient energy optimization and scheduling in industrial factories depend on accurate, reasonable, and real-time monitoring of equipment power consumption. However, the power prediction of industrial equipment requires a large number of process data, which could be inevitably contaminated by some imperfect data, due to the harsh environments. Monitoring or predicting equipment power consumption is usually not feasible using data-driven black-box models with imperfect data. This paper proposes a prediction-based approach for power consumption monitoring using an interpretable data-driven model. First, a data preprocessing method is used to remove outliers and fill in missing values. Then, a Volterra polynomial basis function (VPBF) model is built to predict equipment power consumption. This model decomposes power values into a series of basis functions consisting of input parameters. Moreover, to compensate for data dropouts during the power consumption monitoring process, a networked predictive monitoring system is also proposed. Finally, this paper presents two case studies based on actual production equipment in an industrial manufacturing factory. The results demonstrate that the proposed approach can achieve satisfactory monitoring accuracy and adaptation ability.Note to Practitioners—This paper is motivated by the problem of power consumption monitoring of industrial equipment in harsh production environments. A conventional solution is to predict the power consumption using data-driven black-box models, with limited feasibility and interpretability. This paper proposes a new power consumption monitoring approach, utilizing an interpretable data-driven model and a networked predictive method. This approach accurately reveals the transparent relationships between the output and input parameters. The power prediction result is consequently interpretable and more reasonable. Meanwhile, this approach actively compensates for data dropouts in the network, which can help operators real-time grasp the power consumption of equipment. Furthermore, this approach can be integrated into energy optimization systems as a basis of optimization and decision-making. Practical applications in an aluminum manufacturing company located in Guangdong Province demonstrate that this approach is feasible and applicable. Future research is to expand this approach further for energy optimization and scheduling.

This paper presents a novel flexible capacitive power transfer (CPT) system, which has stable output capability when the coupler is deformed. To obtain the flexible coupler structure, the copper foil with the features of light, thin, and cost-efficiency is employed to form the flexible capacitive coupling. To achieve stable output capability, the frequency band selection method and the frequency splitting characteristic are deduced, which indicates that if the system works on optimized natural resonant frequency, moderate deformation of the copper foil coupler could have negligible effects on output power and efficiency. Furthermore, the finite-element analysis (FEA) is employed to simulate the variation of electric field distribution in the flexible coupler. Finally, a prototype is constructed in the laboratory. The copper foil coupler with 60$\mu \text{m}$thickness and the dimension of$30\times 16$cm is adopted, which can achieve more than 147 W power transfer for a 100 mm air gap with the efficiency over 86%. When the bending displacement is within 40 mm, the fluctuations of received power and efficiency are less than 3.6% and 3.1%, respectively. The measured results validate the feasibility of the proposed CPT system for being applied in flexible scenarios.

This paper proposes an LCCC and CCL compensated wireless servo motor control circuitry utilizing the merits of fundamental and third harmonic. With this circuitry, the motor side needs no additional power supply and controller, which significantly reduces the difficulty of maintenance. Besides, benefiting from the proposing of CCL compensating topology and utilizing of the self-driven circuitry, the secondary operates in fully resonant state and the motor direction control H-bridge is self-driven by the LC frequency selecting circuitry (FSC). Moreover, compared with the conventional motor control circuitry, because of the use of fundamental and third harmonic, the power equalization becomes significantly easier, and zero voltage switch (ZVS) can be realized at the same time; Besides, motion direction control of the motor is simpler with only a single pole double throw (SPDT) at the transmitter side. In addition, system resource consumption is reduced compared with conventional motor drive topologies. In this paper, an 180W prototype is built for experimental verification, and the result shows a good power equalization capability and a transfer efficiency reaching up to 90% at the distance of 10cm.

As a crucial part of cyber-physical systems, a digital twin can process data, visualize processes, and send commands to the control system, which can be used for the research on thermal power plants that are vital for providing energy for manufacturing and industry, and also daily consumptions. This article introduces the methodologies and techniques toward a web-based digital twin thermal power plant. To implement a web-based digital twin thermal power plant, the architecture, modeling, control algorithm, rule model, and physical-digital twin control are explored. The potential functionalities of the web-based digital twin including real-time monitoring, visualization and interactions, and provided services for physical thermal plants and universities are also presented. A case study has been provided to illustrate the web-based digital twin power plant. The research in this article can provide potential solutions for web-based digital twin research and education.

Online experimentation systems that support remote and/or virtual experiments in an Internet-based environment play an important role in skill-enhanced online learning, especially in the field of engineering. This article explores a human-centered online system with a unified architecture that covers the entire process of control engineering experimentation. The control and security-oriented design are presented and the human-centered design including the HTML5-based web application and the configurable graphical user interface is also introduced. Interactive features such as tuning parameters and 3-D animations and interactions are integrated into the system. Enhanced 3-D effects such as anaglyph 3-D and parallax 3-D are also provided. Thus, users can experience different types of 3-D effects on experiment equipment or in a 3-D virtual world while conducting experiments. Details of an application example with a dual tank system are also explored to verify the performance of the proposed system.

Game theory has become popular nowadays in solving the multi-objective optimization problem in the microgrid system. This paper proposes a novel non-cooperative game with hourly dynamic pricing (HDP) that can encourage consumers to participate in energy management. Since the energy demand of consumers in microgrid clusters is stochastic, we use the state clustering scheme to monitor the operating state of loads by smart meters. Moreover, a new utility function with incentive is adopted to reflect the energy cost of consumers. Meanwhile, the utility of microgrid market operators (MMO) can be increased after considering both the stochastic characteristics of microgrids and the cost of power generation. By prioritizing the MMO, simulation results justify the effectiveness of the proposed approach and present new insights toward energy management in the smart grids.

This paper develops a distributed improved finite-time Control algorithm for multi-agent based ac microgrids with battery energy storage systems (BESSs) utilizing a low-width communication network. The proposed control algorithm can simultaneously coordinate BESSs to restore the frequency as well as solving state of charge (SoC) balancing problem. An accelerated convergence can be achieved within a settling time that is irrelevant to initial conditions. Then, the stability of the proposed control algorithm is established using Lyapunov method and homogeneous approximation theory. Further, to significantly reduce the number of communication, an event-triggered mechanism that enable each agent to update its input is provided. It is proved that under the proposed event-triggered consensus control strategy. Finally, we apply the theoretical results to a test system in several case studies with MATLAB/SIMULINK. The results of the theoretical analysis and simulation verify the effectiveness of the proposed strategy.

In this paper, a robust neighbor-based distributed cooperative control strategy is proposed for dc cyber-physical microgrids, considering communication delays and slow switching topologies. The proposed robust control strategy can synchronize the voltages of a dc microgrid to the desired value while achieving the optimal load sharing for minimizing distributed energy resources' (DERs) generation cost to achieve their economic operation at the same layer via a sparse communication network considering communication delays and slow switching topologies synchronously. The continuous interaction of physical-electrical and cyber networks generally exacerbates the occurrence of communication delays. Moreover, the arbitrary switching topologies could destroy the system's transient characteristics at the switching time instants. To further quantify these impacts on the system stability, the communication delay and average switching dwell-time-dependent control conditions for the proposed control strategy are proved based on the Lyapunov-Krasovskii theory. Some sufficient conditions for the exponential stability of the cyber-physical delayed-switching system are developed, which guarantees the robustness of the proposed strategy against the communication delays and dynamically changing interaction topologies. The proposed control protocols are shown to be fully distributed and implemented through a sparse communication network. Finally, several cases on a modified IEEE 34-bus test network are investigated which demonstrate the effectiveness and performance of the results.

The intermittent and stochastic character of the renewable energy resource may lead to instability or even collapse of the distribution network. Against the backdrop, the distributed energy storage unit (DESU) will be a key component to alleviate power fluctuations and frequency instability in the islanded distribution network. However, previous studies mainly account to qualitative analysis of single or multi-machine networks which contain energy storage unit. Motivated by this, we investigate how the DESU affects the frequency stability of the large-scale distribution network. This paper develops a frequency-based control scheme for DESU to change the charging state with frequency. Employing the Kuramoto oscillator model, the dynamic equation of distributed generation (DG) nodes can be obtained to establish an islanded large-scale distribution network model which contains DESU nodes and DG nodes. Through testing the network frequency fluctuation amplitude and the network synchronization time, we explicitly analyze how the DESU enhance the frequency stability of the distribution network. For demonstration purpose, we build a 201-nodes islanded distribution network model which contains DESU nodes and DG nodes to verify the validity of the analysis.

In this paper, we propose a distributed cooperative control strategy for simultaneously coordinating the inverters to eliminate the frequency deviation from the nominal frequency and the BESSs to minimize the power loss using the consensus theory. In addition, the constraints of total power balance and the charging rate (CR) range of each BESS are imposed to illustrate a more practical scenario. Global asymptotic stability of the proposed control algorithm is established with the stability theory based on the equilibrium point. Finally, we apply the theoretical results to a MG with six DERs. The results of the theoretical analysis and simulation indicate that the proposed approach is effective to solve the frequency synchronization and ED problems.

This paper introduces the schemes of remote wiring interactions in an online control laboratory using an HTML5-based virtual 3-D interface. Although virtual laboratories have drawn increasing research attention in the last decade, wiring practice, which is a crucial part of experimentation, is normally neglected. In this paper, a practice including 3-D modeling, HTML5-based rendering, and control algorithm design is implemented for the interactive virtual wiring based on the Networked Control System Laboratory (NCSLab) framework. The wiring practice is combined with control algorithm design, which is already realized in NCSLab, where users are allowed to customize algorithms using MATLAB/Simulink real-time workshop. Apart from remotely implementing the control algorithm, the 3-D virtual wiring process must be completed correctly before the online experiments can proceed properly. The proposed wiring laboratory is evaluated in practical teaching, with both students' performance and perception considered. The conclusions derived from the report show that the 3-D virtual wiring interactions make virtual experimentation complete for simulating a real case, as well as providing an opportunity for a clearer comprehension of control systems.

In this paper, an optimal retail market pricing design for demand response in day-ahead scheduling of smart distribution networks is investigated. Through the well-designed retail market electricity price, the profit of each user is maximized; while the profit of the Distribution Network Operator (DNO) is guaranteed and the risk management model based on the Information Gap Decision Theory (IGDT) method is proposed to protect the DNO from financial risk arising out of the uncertainty of wholesale market prices. Because of the convexity of the problems of users and the DNO, the demand response program is developed based on the Predictor Corrector Proximal Multiplier, which is a distributed algorithm and guaranteed to converge to a global optimal solution, i.e. the retail market electricity price. The numerical simulation is given to show the effectiveness of the proposed pricing method.

The multi-tracking problem of second-order multi-agent system with communication delay is investigated in this paper. An intermittent control protocol is proposed on the basis of only relative position measurement, where the information of agent is quantized before transmission using stochastic quantization scheme. On the basis of algebraic graph theory, stochastic quantization and stability theory, some necessary and sufficient conditions are obtained. It is proved that the multi-agent systems equipped with the designed protocol can track the desired trajectory. Case study illustrates the proposed algorithm can solve the multi-tacking problem effectively.

The classical power allocation/reallocation faces difficult challenges in a future power grid with a great many distributed generators and fast power fluctuations caused by high percentage of renewable energy. To perform power reallocation fast in a future power grid with a large number of participants and disturbances, a hierarchical distributed scheme based on a partition framework is proposed. In the proposed scheme, the power grid is naturally partitioned into a certain number of regions, and the total energy demand in the power grid with disturbances is automatically estimated rather than given in advance. Besides, the centralized local optimizations in regions and the distributed global optimization among regions are coupled to solve the power reallocation problem, in which each region performs as a single agent. Thus, the agents in the proposed scheme are much fewer than the purely distributed ones, hence the communication load is greatly relieved and the reallocation process is significantly simplified. Effectiveness of the proposed scheme is verified by the cases.

This paper presents a robust distributed secondary control (DSC) scheme for inverter-based microgrids (MGs) in a distribution sparse network with uncertain communication links. By using the iterative learning mechanics, two discrete-time DSC controllers are designed, which enable all the distributed energy resources (DERs) in an MG to achieve the voltage/frequency restoration and active power sharing accuracy, respectively. In special, the secondary control inputs are merely updated at the end of each round of iteration, and thus, each DER only needs to share information with its neighbors intermittently in a low-bandwidth communication manner. This way, the communication costs are greatly reduced, and some sufficient conditions on the system stability and robustness to the uncertainties are also derived by using the tools of Lyapunov stability theory, algebraic graph theory, and matrix inequality theory. The proposed controllers are implemented on local DERs, and thus, no central controller is required. Moreover, the desired control objective can also be guaranteed even if all DERs are subject to internal uncertainties and external noises including initial voltage and/or frequency resetting errors and measurement disturbances, which then improves the system reliability and robustness. The effectiveness of the proposed DSC scheme is verified by the simulation of an islanded MG in MATLAB/SimPowerSystems.

In this study, the authors present a clustering‐based thresholding technique for image segmentation. This technique is built on the minimum within‐class variance of a scalable local region that draws upon the previous result and its spatial information to account for the connectivity between the background and the object. The cluster mean derived from the object region in each iteration is considered as an alternative global threshold to prevent the pixels with low intensity from clustering and enable the pixels with similarity to be clustered. This approach makes the method less sensitive to the problem associated with the shape of the histogram and thus leads to an automatic iterated method for a promising segmentation performance. Experiment results on synthetic and real images prove the efficiency of the method.